Tests grid search

multiview_platform/mono_multi_view_classifiers/utils/hyper_parameter_search.py

@@ -16,59 +16,35 @@ from .base import get_metric
 from .. import metrics
 
 
-def search_best_settings(dataset_var, labels, classifier_module,
-                         classifier_name,
-                         metrics, learning_indices, i_k_folds, random_state,
-                         directory, views_indices=None, nb_cores=1,
-                         searching_tool="randomized_search-equiv", n_iter=1,
-                         classifier_config=None):
-    """Used to select the right hyper-parameter optimization function
-    to optimize hyper parameters"""
-    if views_indices is None:
-        views_indices = list(range(dataset_var.get_nb_view))
-    output_file_name = directory
-    thismodule = sys.modules[__name__]
-    if searching_tool is not "None":
-        searching_tool_method = getattr(thismodule,
-                                        searching_tool.split("-")[0])
-        best_settings, scores, params = searching_tool_method(
-            dataset_var, labels, "multiview", random_state, output_file_name,
-            classifier_module, classifier_name, i_k_folds,
-            nb_cores, metrics, n_iter, classifier_config,
-            learning_indices=learning_indices, view_indices=views_indices,
-            equivalent_draws=searching_tool.endswith("equiv"))
-        gen_report(params, scores, directory, )
-    else:
-        best_settings = classifier_config
-    return best_settings  # or well set clasifier ?
+# def search_best_settings(dataset_var, labels, classifier_module,
+#                          classifier_name,
+#                          metrics, learning_indices, i_k_folds, random_state,
+#                          directory, views_indices=None, nb_cores=1,
+#                          searching_tool="randomized_search-equiv", n_iter=1,
+#                          classifier_config=None):
+#     """Used to select the right hyper-parameter optimization function
+#     to optimize hyper parameters"""
+#     if views_indices is None:
+#         views_indices = list(range(dataset_var.get_nb_view))
+#     output_file_name = directory
+#     thismodule = sys.modules[__name__]
+#     if searching_tool is not "None":
+#         searching_tool_method = getattr(thismodule,
+#                                         searching_tool.split("-")[0])
+#         best_settings, scores, params = searching_tool_method(
+#             dataset_var, labels, "multiview", random_state, output_file_name,
+#             classifier_module, classifier_name, i_k_folds,
+#             nb_cores, metrics, n_iter, classifier_config,
+#             learning_indices=learning_indices, view_indices=views_indices,
+#             equivalent_draws=searching_tool.endswith("equiv"))
+#         gen_report(params, scores, directory, )
+#     else:
+#         best_settings = classifier_config
+#     return best_settings  # or well set clasifier ?
 
 
 class HPSearch:
 
-    # def __init__(self, y, framework, random_state, output_file_name,
-    #                   classifier_module,
-    #                   classifier_name, folds=4, nb_cores=1,
-    #                   metric = [["accuracy_score", None]],
-    #                   classifier_kwargs={}, learning_indices=None,
-    #                   view_indices=None,
-    #                   track_tracebacks=True):
-    #     estimator = getattr(classifier_module, classifier_name)(
-    #         random_state=random_state,
-    #         **classifier_kwargs)
-    #     self.init_params()
-    #     self.estimator = get_mc_estim(estimator, random_state,
-    #                              multiview=(framework == "multiview"),
-    #                              y=y)
-    #     self.folds = folds
-    #     self.nb_cores = nb_cores
-    #     self.clasifier_kwargs = classifier_kwargs
-    #     self.learning_indices = learning_indices
-    #     self.view_indices = view_indices
-    #     self.output_file_name = output_file_name
-    #     metric_module, metric_kwargs = get_metric(metric)
-    #     self.scorer = metric_module.get_scorer(**metric_kwargs)
-    #     self.track_tracebacks = track_tracebacks
-
     def get_scoring(self, metric):
         if isinstance(metric, list):
             metric_module, metric_kwargs = get_metric(metric)
@@ -137,11 +113,6 @@ class HPSearch:
         self.n_splits_ = n_splits
         return self
 
-    @abstractmethod
-    def init_params(self):
-        self.params_dict = {}
-        raise NotImplementedError
-
     @abstractmethod
     def get_candidate_params(self, X):
         raise NotImplementedError
@@ -203,9 +174,6 @@ class Random(RandomizedSearchCV, HPSearch):
             return HPSearch.fit_multiview(self, X, y=y, groups=groups,
                                            **fit_params)
 
-    # def init_params(self,):
-    #     self.params_dict = self.estimator.gen_distribs()
-
     def get_candidate_params(self, X):
         if self.equivalent_draws:
             self.n_iter = self.n_iter * X.nb_view

multiview_platform/tests/test_utils/test_hyper_parameter_search.py

@@ -49,7 +49,7 @@ class FakeEstimMV(BaseEstimator):
 
 
 
-class Test_MultiviewCompatibleRandomizedSearchCV(unittest.TestCase):
+class Test_Random(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
@@ -142,6 +142,27 @@ class Test_MultiviewCompatibleRandomizedSearchCV(unittest.TestCase):
         self.assertEqual(RSCV.best_params_["param1"], "return exact")
 
 
+class Test_Grid(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        cls.parameter_grid = {"param1":[5,6], "param2":[7,8]}
+        cls.estimator = FakeEstim()
+
+    def test_simple(self):
+        grid = hyper_parameter_search.Grid(self.estimator,
+                                           param_grid=self.parameter_grid)
+
+    def test_get_candidate_params(self):
+        grid = hyper_parameter_search.Grid(self.estimator,
+                                           param_grid=self.parameter_grid)
+        grid.get_candidate_params(None)
+        self.assertEqual(grid.candidate_params, [{"param1": 5, "param2": 7},
+                                                 {"param1": 5, "param2": 8},
+                                                 {"param1": 6, "param2": 7},
+                                                 {"param1": 6, "param2": 8}])
+
+
 # if __name__ == '__main__':
 #     # unittest.main()
 #     suite = unittest.TestLoader().loadTestsFromTestCase(Test_randomized_search)